Abstract: Models are central in reasoning about physical systesm. While many studies of
model based reasoning in Artificial Intelligence have focused on fundamental
reasoning techniques, only recently have attempts been made to study the
principles of model construction. The tradeoff between quality and efficiency
in reasoning produces one important issue in model development - how to build
models that contain enough information (good approximation) for the reasoning
and yet do not contain so much detail (good abstraction) that searching would
become intractable. The problem becomes even more pronounced when dealing
with large and complex systems. One common practice is to decompose systems
into subdivisions where problem-solving can be broken down into smaller
problems. However, decomposability alone is not enough. The organization of
models must provide integration of various reasoning results to allow coherent
reasoning about the overall system. Another approach to help us manage
complex systems with a high computational cost is by reasoning with multiple
appropriate models. This allows us to reason at multiple levels of
abstraction. For example, different models of a device correspond to
different abstractions of it. Each model is well suited to a particular class
of uses - both in terms of the answers it can provide and in terms of
efficiency of the inference. However, as the systems get larger and more
complicated, complete instantiation becomes both undesirable and impossible.
To handle this problem one needs to know what is relevant and what models to
select. All of these topics are currently under research in model-based
reasoning aobut large and complex systems.